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Ramayo-Caldas Y, Crespo-Piazuelo D, Morata J, González-Rodríguez O, Sebastià C, Castello A, Dalmau A, Ramos-Onsins S, Alexiou KG, Folch JM, Quintanilla R, Ballester M. Copy Number Variation on ABCC2-DNMBP Loci Affects the Diversity and Composition of the Fecal Microbiota in Pigs. Microbiol Spectr 2023; 11:e0527122. [PMID: 37255458 PMCID: PMC10433821 DOI: 10.1128/spectrum.05271-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 05/16/2023] [Indexed: 06/01/2023] Open
Abstract
Genetic variation in the pig genome partially modulates the composition of porcine gut microbial communities. Previous studies have been focused on the association between single nucleotide polymorphisms (SNPs) and the gut microbiota, but little is known about the relationship between structural variants and fecal microbial traits. The main goal of this study was to explore the association between porcine genome copy number variants (CNVs) and the diversity and composition of pig fecal microbiota. For this purpose, we used whole-genome sequencing data to undertake a comprehensive identification of CNVs followed by a genome-wide association analysis between the estimated CNV status and the fecal bacterial diversity in a commercial Duroc pig population. A CNV predicted as gain (DUP) partially harboring ABCC2-DNMBP loci was associated with richness (P = 5.41 × 10-5, false discovery rate [FDR] = 0.022) and Shannon α-diversity (P = 1.42 × 10-4, FDR = 0.057). The in silico predicted gain of copies was validated by real-time quantitative PCR (qPCR), and its segregation, and positive association with the richness and Shannon α-diversity of the porcine fecal bacterial ecosystem was confirmed in an unrelated F1 (Duroc × Iberian) cross. Our results advise the relevance of considering the role of host-genome structural variants as potential modulators of microbial ecosystems and suggest the ABCC2-DNMBP CNV as a host-genetic factor for the modulation of the diversity and composition of the fecal microbiota in pigs. IMPORTANCE A better understanding of the environmental and host factors modulating gut microbiomes is a topic of greatest interest. Recent evidence suggests that genetic variation in the pig genome partially controls the composition of porcine gut microbiota. However, since previous studies have been focused on the association between single nucleotide polymorphisms and the fecal microbiota, little is known about the relationship between other sources of genetic variation, like the structural variants and microbial traits. Here, we identified, experimentally validated, and replicated in an independent population a positive link between the gain of copies of ABCC2-DNMBP loci and the diversity and composition of pig fecal microbiota. Our results advise the relevance of considering the role of host-genome structural variants as putative modulators of microbial ecosystems and open the possibility of implementing novel holobiont-based management strategies in breeding programs for the simultaneous improvement of microbial traits and host performance.
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Affiliation(s)
- Yuliaxis Ramayo-Caldas
- Animal Breeding and Genetics Program, Institute of Agrifood Research and Technology, Caldes de Montbui, Spain
| | - Daniel Crespo-Piazuelo
- Animal Breeding and Genetics Program, Institute of Agrifood Research and Technology, Caldes de Montbui, Spain
| | - Jordi Morata
- Centro Nacional de Análisis Genómico, Centre for Genomic Regulation, Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Olga González-Rodríguez
- Animal Breeding and Genetics Program, Institute of Agrifood Research and Technology, Caldes de Montbui, Spain
| | - Cristina Sebastià
- Plant and Animal Genomics Program, Centre for Research in Agricultural Genomics, Consejo Superior de Investigaciones Científicas (CSIC)-Institute of Agrifood Research and Technology-Autonomous University of Barcelona-UB, Bellaterra, Spain
- Animal and Food Science Department, Autonomous University of Barcelona, Bellaterra, Spain
| | - Anna Castello
- Plant and Animal Genomics Program, Centre for Research in Agricultural Genomics, Consejo Superior de Investigaciones Científicas (CSIC)-Institute of Agrifood Research and Technology-Autonomous University of Barcelona-UB, Bellaterra, Spain
- Animal and Food Science Department, Autonomous University of Barcelona, Bellaterra, Spain
| | - Antoni Dalmau
- Animal Welfare Program, Institute of Agrifood Research and Technology, Girona, Spain
| | - Sebastian Ramos-Onsins
- Plant and Animal Genomics Program, Centre for Research in Agricultural Genomics, Consejo Superior de Investigaciones Científicas (CSIC)-Institute of Agrifood Research and Technology-Autonomous University of Barcelona-UB, Bellaterra, Spain
| | - Konstantinos G. Alexiou
- Plant and Animal Genomics Program, Centre for Research in Agricultural Genomics, Consejo Superior de Investigaciones Científicas (CSIC)-Institute of Agrifood Research and Technology-Autonomous University of Barcelona-UB, Bellaterra, Spain
| | - Josep M. Folch
- Plant and Animal Genomics Program, Centre for Research in Agricultural Genomics, Consejo Superior de Investigaciones Científicas (CSIC)-Institute of Agrifood Research and Technology-Autonomous University of Barcelona-UB, Bellaterra, Spain
- Animal and Food Science Department, Autonomous University of Barcelona, Bellaterra, Spain
| | - Raquel Quintanilla
- Animal Breeding and Genetics Program, Institute of Agrifood Research and Technology, Caldes de Montbui, Spain
| | - Maria Ballester
- Animal Breeding and Genetics Program, Institute of Agrifood Research and Technology, Caldes de Montbui, Spain
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Huang S, Ailer E, Kilbertus N, Pfister N. Supervised learning and model analysis with compositional data. PLoS Comput Biol 2023; 19:e1011240. [PMID: 37390111 DOI: 10.1371/journal.pcbi.1011240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 06/03/2023] [Indexed: 07/02/2023] Open
Abstract
Supervised learning, such as regression and classification, is an essential tool for analyzing modern high-throughput sequencing data, for example in microbiome research. However, due to the compositionality and sparsity, existing techniques are often inadequate. Either they rely on extensions of the linear log-contrast model (which adjust for compositionality but cannot account for complex signals or sparsity) or they are based on black-box machine learning methods (which may capture useful signals, but lack interpretability due to the compositionality). We propose KernelBiome, a kernel-based nonparametric regression and classification framework for compositional data. It is tailored to sparse compositional data and is able to incorporate prior knowledge, such as phylogenetic structure. KernelBiome captures complex signals, including in the zero-structure, while automatically adapting model complexity. We demonstrate on par or improved predictive performance compared with state-of-the-art machine learning methods on 33 publicly available microbiome datasets. Additionally, our framework provides two key advantages: (i) We propose two novel quantities to interpret contributions of individual components and prove that they consistently estimate average perturbation effects of the conditional mean, extending the interpretability of linear log-contrast coefficients to nonparametric models. (ii) We show that the connection between kernels and distances aids interpretability and provides a data-driven embedding that can augment further analysis. KernelBiome is available as an open-source Python package on PyPI and at https://github.com/shimenghuang/KernelBiome.
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Affiliation(s)
- Shimeng Huang
- Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark
| | | | - Niki Kilbertus
- Helmholtz Munich, Munich, Germany
- Technical University of Munich, Munich, Germany
| | - Niklas Pfister
- Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark
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Josephs N, Lin L, Rosenberg S, Kolaczyk ED. Bayesian classification, anomaly detection, and survival analysis using network inputs with application to the microbiome. Ann Appl Stat 2023. [DOI: 10.1214/22-aoas1623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
| | - Lizhen Lin
- Department of Applied and Computational Mathematics and Statistics, The University of Notre Dame
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Ramayo-Caldas Y, Zingaretti LM, Pérez-Pascual D, Alexandre PA, Reverter A, Dalmau A, Quintanilla R, Ballester M. Leveraging host-genetics and gut microbiota to determine immunocompetence in pigs. Anim Microbiome 2021; 3:74. [PMID: 34689834 PMCID: PMC8543910 DOI: 10.1186/s42523-021-00138-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 10/12/2021] [Indexed: 01/13/2023] Open
Abstract
Background The gut microbiota influences host performance playing a relevant role in homeostasis and function of the immune system. The aim of the present work was to identify microbial signatures linked to immunity traits and to characterize the contribution of host-genome and gut microbiota to the immunocompetence in healthy pigs. Results To achieve this goal, we undertook a combination of network, mixed model and microbial-wide association studies (MWAS) for 21 immunity traits and the relative abundance of gut bacterial communities in 389 pigs genotyped for 70K SNPs. The heritability (h2; proportion of phenotypic variance explained by the host genetics) and microbiability (m2; proportion of variance explained by the microbial composition) showed similar values for most of the analyzed immunity traits, except for both IgM and IgG in plasma that was dominated by the host genetics, and the haptoglobin in serum which was the trait with larger m2 (0.275) compared to h2 (0.138). Results from the MWAS suggested a polymicrobial nature of the immunocompetence in pigs and revealed associations between pigs gut microbiota composition and 15 of the analyzed traits. The lymphocytes phagocytic capacity (quantified as mean fluorescence) and the total number of monocytes in blood were the traits associated with the largest number of taxa (6 taxa). Among the associations identified by MWAS, 30% were confirmed by an information theory network approach. The strongest confirmed associations were between Fibrobacter and phagocytic capacity of lymphocytes (r = 0.37), followed by correlations between Streptococcus and the percentage of phagocytic lymphocytes (r = -0.34) and between Megasphaera and serum concentration of haptoglobin (r = 0.26). In the interaction network, Streptococcus and percentage of phagocytic lymphocytes were the keystone bacterial and immune-trait, respectively. Conclusions Overall, our findings reveal an important connection between gut microbiota composition and immunity traits in pigs, and highlight the need to consider both sources of information, host genome and microbial levels, to accurately characterize immunocompetence in pigs. Supplementary Information The online version contains supplementary material available at 10.1186/s42523-021-00138-9.
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Affiliation(s)
- Yuliaxis Ramayo-Caldas
- Animal Breeding and Genetics Program, IRTA, Torre Marimón, 08140, Caldes de Montbui, Barcelona, Spain.
| | - Laura M Zingaretti
- Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB, Universitat Autònoma de Barcelona, 08193, Bellaterra, Spain
| | - David Pérez-Pascual
- Unité de Génétique des Biofilms, Institut Pasteur, UMR CNRS2001, Paris, France
| | | | - Antonio Reverter
- CSIRO Agriculture and Food, St. Lucia, Brisbane, QLD, 4067, Australia
| | - Antoni Dalmau
- Animal Welfare Subprogram, IRTA, 17121, Monells, Girona, Spain
| | - Raquel Quintanilla
- Animal Breeding and Genetics Program, IRTA, Torre Marimón, 08140, Caldes de Montbui, Barcelona, Spain
| | - Maria Ballester
- Animal Breeding and Genetics Program, IRTA, Torre Marimón, 08140, Caldes de Montbui, Barcelona, Spain.
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Velasco-Galilea M, Piles M, Ramayo-Caldas Y, Sánchez JP. The value of gut microbiota to predict feed efficiency and growth of rabbits under different feeding regimes. Sci Rep 2021; 11:19495. [PMID: 34593949 PMCID: PMC8484599 DOI: 10.1038/s41598-021-99028-y] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 09/13/2021] [Indexed: 02/08/2023] Open
Abstract
Gut microbiota plays an important role in nutrient absorption and could impact rabbit feed efficiency. This study aims at investigating such impact by evaluating the value added by microbial information for predicting individual growth and cage phenotypes related to feed efficiency. The dataset comprised individual average daily gain and cage-average daily feed intake from 425 meat rabbits, in which cecal microbiota was assessed, and their cage mates. Despite microbiota was not measured in all animals, consideration of pedigree relationships with mixed models allowed the study of cage-average traits. The inclusion of microbial information into certain mixed models increased their predictive ability up to 20% and 46% for cage-average feed efficiency and individual growth traits, respectively. These gains were associated with large microbiability estimates and with reductions in the heritability estimates. However, large microbiabililty estimates were also obtained with certain models but without any improvement in their predictive ability. A large proportion of OTUs seems to be responsible for the prediction improvement in growth and feed efficiency traits, although specific OTUs taxonomically assigned to 5 different phyla have a higher weight. Rabbit growth and feed efficiency are influenced by host cecal microbiota, thus considering microbial information in models improves the prediction of these complex phenotypes.
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Affiliation(s)
- María Velasco-Galilea
- grid.8581.40000 0001 1943 6646Animal Breeding and Genetics, Institute of Agrifood Research and Technology (IRTA), Caldes de Montbui, 08140 Barcelona, Spain
| | - Miriam Piles
- grid.8581.40000 0001 1943 6646Animal Breeding and Genetics, Institute of Agrifood Research and Technology (IRTA), Caldes de Montbui, 08140 Barcelona, Spain
| | - Yuliaxis Ramayo-Caldas
- grid.8581.40000 0001 1943 6646Animal Breeding and Genetics, Institute of Agrifood Research and Technology (IRTA), Caldes de Montbui, 08140 Barcelona, Spain
| | - Juan P. Sánchez
- grid.8581.40000 0001 1943 6646Animal Breeding and Genetics, Institute of Agrifood Research and Technology (IRTA), Caldes de Montbui, 08140 Barcelona, Spain
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